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Kernel Metadata Declaration — on demand

ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.

Reads the DOI's public Crossref record; nothing is stored. Until Smart Scholars is accredited, every Declaration is marked as a draft in the XML itself.

✓ Meets every rule of the format

The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.

What the DOI identifies

Predicting Air Pollutant using Data Mining and Machine Learning Algorithms

10.46243/jst.2021.v6.i04.pp25-30 · JournalArticle — an article in a journal · Digital · Visual · Language · en

Published 2021-08-16

Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 25–30

Principal agents

  • Isha Jagtap (author → Author)
  • Prof. Nandini Babbar (author → Author)
  • Longman Publishers (publisher → Publisher)

Also in the record, outside the Kernel: the abstract, the licence, 4 links, 9 references. Source: Crossref (member 25296), registered 2026-09-11, last deposited 2026-09-20.

⬇ Record (JSON) ⬇ Declaration (XML)

System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1

Each element by the standard's name and the Handbook's (in grey), read off the record.

DOI Name
DOI name
10.46243/jst.2021.v6.i04.pp25-30
Referent Type
referentType
Creation
Referent Sub-Type
referentSubType
JournalArticle — an article in a journal
Referent Name(s)
referentName(s)
Predicting Air Pollutant using Data Mining and Machine Learning Algorithms (PrincipalTitle, en)
Basic Metadata
basicMetadata
author: Isha Jagtap
author: Prof. Nandini Babbar
publisher: Longman Publishers
published: 2021-08-16
part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 06 · no. 01 · pp. 25–30
language: en
form: Digital · Visual · Language
Referent Identifier(s)
alternateIdentifier(s)
none besides the DOI
Registration Authority
registrationAuthorityCode
Crossref — issued by Crossref (member 25296); held here as a copy
Created Date
issueDate
2026-09-11
relatedIdentifiersnone needed — the descriptive metadata is in this record

The record

{
    "format": "smartscholars-doi-metadata/1.0",
    "doi": "10.46243/jst.2021.v6.i04.pp25-30",
    "referent": "Creation",
    "type": "JournalArticle",
    "structural_type": "Digital",
    "modes": [
        "Visual"
    ],
    "characters": [
        "Language"
    ],
    "titles": [
        {
            "value": "Predicting Air Pollutant using Data Mining and Machine Learning Algorithms",
            "type": "PrincipalTitle",
            "lang": "en"
        }
    ],
    "identifiers": [
        {
            "type": "DOI",
            "value": "10.46243/jst.2021.v6.i04.pp25-30"
        }
    ],
    "agents": [
        {
            "role": "author",
            "name": {
                "given": "Isha",
                "family": "Jagtap"
            },
            "sequence": "first"
        },
        {
            "role": "author",
            "name": {
                "given": "Prof. Nandini",
                "family": "Babbar"
            },
            "sequence": "additional"
        },
        {
            "role": "publisher",
            "name": {
                "org": "Longman Publishers"
            }
        }
    ],
    "dates": {
        "published": "2021-08-16",
        "date_type": "PublicationDate",
        "online": "2021-08-16"
    },
    "language": "en",
    "container": {
        "type": "Journal",
        "titles": [
            {
                "value": "Journal of Science & Technology",
                "type": "PrincipalTitle"
            }
        ],
        "identifiers": [
            {
                "type": "ISSN",
                "value": "2456-5660",
                "medium": "electronic"
            }
        ],
        "volume": "06",
        "issue": "01",
        "pages": {
            "first": "25",
            "last": "30"
        }
    },
    "links": [
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/758",
            "return_type": "text/html",
            "primary": true
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/758/681",
            "purpose": "text-mining",
            "return_type": "application/pdf"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/download/758/2786",
            "purpose": "text-mining",
            "return_type": "application/xml"
        },
        {
            "url": "https://www.jst.org.in/index.php/pub/article/view/758/681",
            "purpose": "similarity-checking"
        }
    ],
    "abstract": {
        "value": "Air pollution can be defined presence of harmful or hazardous substances in the air which deteriorate the quality of air. As we are moving ahead in future the environment is getting polluted day by day due to these biological molecules and harmful gases. These pollutant causes diseases, allergy, etc and death as well. The main aim of this article is to study data mining and machine learning algorithms for predicting air pollutants, especially PM2.5 .So as to control the emission of these harmful substances This is a scientific approach for predicting PM2.5 level in the air using a data set containing different attributes.",
        "lang": "en"
    },
    "license": {
        "url": "https://creativecommons.org/licenses/by/4.0/",
        "start": "2021-08-16",
        "applies_to": "vor"
    },
    "references": [
        {
            "key": "ref1",
            "doi": "10.1515/amcs-2016-0033",
            "unstructured": "Krzysztof Siwek, Stanislaw Osowski, “Data Mining methods for prediction of Air Pollution,” Int. J. Appl. Math. Computer. Sci.,2016"
        },
        {
            "key": "ref2",
            "doi": "10.14445/22315381/ijett-v59p238",
            "unstructured": "Aditya C R, Chandana R Deshmukh, Nayana D K, Praveen Gandhi Vidyavastu\" Detection and Prediction of Air Pollution using Machine Learning Models\", International Journal of Engineering Trends and Technology (IJETT),2018"
        },
        {
            "key": "ref3",
            "doi": "10.1109/icsai.2018.8599314",
            "unstructured": "W. Wang, W. Shen, B. Chen, R. Zhu and Y. Sun, \"Air Quality Index Forecasting Based on SVM and Moments,\" 5th International Conference on Systems and Informatics (ICSAI), Nanjing,2018"
        },
        {
            "key": "ref4",
            "doi": "10.1016/s1352-2310(01)00124-8",
            "unstructured": "Marcazzan, G.M.; Vaccaro, S.; Valli, G. Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy). Atmos. Environ. 2001, 35, 4639–4650"
        },
        {
            "key": "ref5",
            "doi": "10.1109/ictus.2017.8286113",
            "unstructured": "Aggarwal, A.; Choudhary, T.; Kumar, P. A fuzzy interface system for determining Air Quality Index. In Proceedings of the 2017 International Conference on Infocom Technologies and Unmanned Systems, Dubai, UAE, 18–20 December 2017; pp. 786–790"
        },
        {
            "key": "ref6",
            "doi": "10.4236/jep.2013.412a1001",
            "unstructured": "Azman Azid1,Hafizan Juahir1* MohdTalib Latif2,Sharifuddin Mohd Zain3, Mohamad Romizan Osman,\"Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia\".Journal of Environmental Protection, 2013, 4, 1-10"
        },
        {
            "key": "ref7",
            "doi": "10.1109/idap.2018.8620768",
            "unstructured": "Altinçöp, H.; Oktay, A.B. Air Pollution Forecasting with Random Forest Time Series Analysis. In Proceedings of the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, 28–29 September 2018"
        },
        {
            "key": "ref8",
            "doi": "10.1007/978-3-642-37807-2_9",
            "unstructured": "N. Loya, et al., “Forecast of Air Quality Based on Ozone by Decision Trees and Neural Networks” Mexican International Conference on Artificial Intelligence (MICAI), pp 97-106, 2012"
        },
        {
            "key": "ref9",
            "doi": "10.17762/turcomat.v12i2.2025",
            "unstructured": "Makrand M Jadhav, Gajanan H. Chavan, and Altaf O. Mulani, “Machine Learning based Autonomous Fire Combat Turret”, Turkish Journal of Computer and Mathematics Education, Vol.12 No.2 (2021), 2372-2381, https://doi.org/10.17762/turcomat.v12i2.2025"
        }
    ],
    "record": {
        "registrant": "Longman Publishers",
        "registered": "2026-09-11",
        "updated": "2026-09-20",
        "issue_number": 1,
        "source": "crossref-api",
        "source_agency": "Crossref (member 25296)"
    }
}

The Kernel Metadata Declaration

DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation

<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
  <referentDoiName>10.46243/jst.2021.v6.i04.pp25-30</referentDoiName>
  <primaryReferentType>Creation</primaryReferentType>
  <registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
  <issueDate>2026-10-04</issueDate>
  <issueNumber>1</issueNumber>
  <referentCreation>
    <name primaryLanguage="en">
      <value>Predicting Air Pollutant using Data Mining and Machine Learning Algorithms</value>
      <type>PrincipalTitle</type>
    </name>
    <identifier>
      <nonUriValue>10.46243/jst.2021.v6.i04.pp25-30</nonUriValue>
      <uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2021.v6.i04.pp25-30</uri>
      <type>DOI</type>
    </identifier>
    <identifier>
      <uri returnType="text/html">https://www.jst.org.in/index.php/pub/article/view/758</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/download/758/681</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri returnType="application/xml">https://www.jst.org.in/index.php/pub/article/download/758/2786</uri>
      <type>URI</type>
    </identifier>
    <identifier>
      <uri>https://www.jst.org.in/index.php/pub/article/view/758/681</uri>
      <type>URI</type>
    </identifier>
    <structuralType>Digital</structuralType>
    <mode>Visual</mode>
    <character>Language</character>
    <type>JournalArticle</type>
    <principalAgent>
      <name>
        <value>Isha Jagtap</value>
        <type>PrincipalName</type>
      </name>
      <role>Author</role>
    </principalAgent>
    <principalAgent>
      <name>
        <value>Prof. Nandini Babbar</value>
        <type>PrincipalName</type>
      </name>
      <role>Author</role>
    </principalAgent>
    <principalAgent>
      <name>
        <value>Longman Publishers</value>
        <type>PrincipalName</type>
      </name>
      <role>Publisher</role>
    </principalAgent>
    <linkedCreation>
      <name>
        <value>Journal of Science &amp; Technology</value>
        <type>PrincipalTitle</type>
      </name>
      <identifier>
        <nonUriValue>2456-5660</nonUriValue>
        <type>ISSN</type>
      </identifier>
      <referentCreationRole>Part</referentCreationRole>
      <referentCreationSequenceIdentifier>
        <value>06</value>
        <type>VolumeNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>01</value>
        <type>IssueNumber</type>
      </referentCreationSequenceIdentifier>
      <referentCreationSequenceIdentifier>
        <value>25-30</value>
        <type>PageNumber</type>
      </referentCreationSequenceIdentifier>
    </linkedCreation>
    <language>en</language>
    <languageOfReferentContent>
      <language>en</language>
      <languageOfReferentContentType>Original</languageOfReferentContentType>
    </languageOfReferentContent>
    <creationDate>
      <date>2021-08-16</date>
      <creationDateType>PublicationDate</creationDateType>
    </creationDate>
  </referentCreation>
</kernelMetadata>
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